Extraction of Visual Features for Recommendation of Products via Deep Learning

Андреева, Елена; Ignatov, Dmitry I.; Grachev, Artem M.; Savchenko, Andrey V.

doi:10.1007/978-3-030-11027-7_20

Cited by 14 publications

(3 citation statements)

References 10 publications

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“…However, product retrieval is more complicated than simple image retrieval due to the different shooting angles, conditions, backgrounds, or postures of the images [165][166][167][168][169]. For instance, clothing images taken on the street or in a store with a phone may differ from those in databases and e-commerce websites.…”

Section: Spark Creative Inspirationmentioning

confidence: 99%

Big Data and AI-driven Product Design: A Survey

Quan

Zeng

et al. 2023

Preprint

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As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI driven product design methodology with significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review on big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited in aiding product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design.

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Section: Spark Creative Inspirationmentioning

confidence: 99%

Big Data and AI-driven Product Design: A Survey

Quan

Zeng

et al. 2023

Preprint

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“…The retrieval input can be text, images, or both of them [162,163,164,165,166]. For product, the input image provided by designers and users may be taken by their phone on the street or in a store, which is quite different from image databases and e-commerce websites in terms of shooting angle, condition, background, or posture [167,168,169,170,171]. Therefore, product retrieval is more complicated than a simple image retrieval [172,173,174].…”

Section: Product Design Based On Image Datamentioning

confidence: 99%

Big Data driven Product Design: A Survey

Quan¹,

Li²,

Zeng³

et al. 2021

Preprint

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With the improvement of living standards, user requirements of modern products are becoming increasingly more diversified and personalized. Traditional product design methods can no longer satisfy the market needs due to their strong subjectivity, small survey scope, poor real-time data, and lack of visual display, which calls for the development of big data driven product design methodology. Big data in the product lifecycle contains valuable information for guiding product design, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements; product images contain information of shape,color, and texture which can inspire designers to get initial design schemes more quickly or even directly generate new product images. How to efficiently collect product design related data and exploit them effectively during the whole product design process is thus critical to modern product design. This paper aims to conduct a comprehensive survey on big data driven product design. It will help researchers and practitioners to comprehend the latest development of relevant studies and applications centered on how big data can be processed, analyzed, and exploited in aiding product design. We first introduce several representative traditional product design methods and highlight their limitations. Then we discuss current and potential applications of textual data, image data, audio data, and video data in product design cycles. Finally, major deficiencies of existing data driven product design studies and future research directions are summarized. We believe that this study can draw increasing attention to modern data driven product design.Keywords product design • big data • Kansei engineering • QFD • generative design * Correspondence.

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“…The VisNet architecture with parallel shallow neural net and VGG16 convolutional neural network (CNN) was fine-tuned like a siamese net, taking input triplets of a query image, a similar image and a negative example [14]. The clothing, shoes and jewelry from Amazon product dataset are recognized in [1] by an extraction of ResNet-based visual features and a special shallow net. Visual search and recommendations are implemented on Pinterest [21] using Web-scale object detection and indexing with very deep CNNs [3].…”

Section: Introductionmentioning

confidence: 99%

User Preference Prediction in Visual Data on Mobile Devices

Savchenko¹

2021

2021 International Joint Conference on Neural Networks (IJCNN)

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In this paper we consider the user modeling given the photos and videos from the gallery on a mobile device. We propose the novel user preference prediction engine based on scene understanding, object detection and face recognition. At first, all faces in a gallery are clustered and all private photos and videos with faces from large clusters are processed on the embedded system in offline mode. Other photos are sent to the remote server to be analyzed by very deep models. The visual features of each photo are aggregated into a single user descriptor using the neural attention block. The proposed pipeline is implemented for the Android mobile platform. Experimental results with a subset of Amazon Home and Kitchen, Places2 and Open Images datasets demonstrate the possibility to process images very efficiently without accuracy degradation.

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Extraction of Visual Features for Recommendation of Products via Deep Learning

Cited by 14 publications

References 10 publications

Big Data and AI-driven Product Design: A Survey

Big Data and AI-driven Product Design: A Survey

Big Data driven Product Design: A Survey

User Preference Prediction in Visual Data on Mobile Devices

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